Soft sensors are widely constructed in process industry to realize process monitoring, quality prediction, and many other important applications. With the development of hardware and software, industrial processes have embraced new characteristics, which lead to the poor performance of traditional soft sensor modeling methods. Deep learning, as a kind of data-driven approach, shows its great potential in many fields, as well as in soft sensing scenarios. After a period of development, especially in the last five years, many new issues have emerged that need to be investigated. Therefore, in this article, the necessity and significance of deep learning for soft sensor applications are demonstrated first by analyzing the merits of deep learning and the trends of industrial processes. Next, mainstream deep learning models, tricks, and frameworks/toolkits are summarized and discussed to help designers propel the developing progress of soft sensors. Then, existing works are reviewed and analyzed to discuss the demands and problems occurred in practical applications. Finally, outlook and conclusions are given.

A Survey on Deep Learning for Data-Driven Soft Sensors

Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism

A digital twin (DT) is classically defined as the virtual replica of a real-world product, system, being, communities, even cities that are continuously updated with data from its physical counterpart, as well as its environment. It bridges the virtual cyberspace with the physical entities and, as such, is considered to be the pillar of Industry 4.0 and the innovation backbone of the future. A DT is created and used throughout the whole life cycle of the entity it replicates, from cradle to grave, so to speak. This article focuses on the present state of the art of DTs, concentrating on the use of DTs in industry in the context of smart manufacturing, especially from the point of view of plantwide optimization. The main capabilities of DTs (mirroring, shadowing and threading) are discussed in this context. The article concludes with a perspective on the future. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Industrial applications of digital twins

An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty

Prediction of material removal rate in chemical mechanical polishing via residual convolutional neural network

Over the past twenty years, numerous research outcomes have been published, related to the design and implementation of soft sensors. In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. However, novel opportunities are accompanied by novel challenges. This work is motivated by these observations and aims to present a comprehensive review of the developments since the start of the millennium. While a few books and review articles are published on the related topics, more focus on the most up-to-the-date advancement is put in this work, from the perspective of systems and control.

A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes

The fourth industrial revolution is elevating the overall flexibility and controllability of the production processes, leading to better consistency in quality and improved feasibility of personalization. However, an evident downside is observed from the boosting system openness because the security and safety protocols are not ready for the potential impacts and threats. In this work, an in-depth analysis about the novel challenges is firstly presented. Then, the mechanism of false data injection attack on the industrial cyber-physical systems is studied. The target is to reveal the condition when the existing fault diagnosis systems fail to detect the false data injection attack on the distributed and networked systems. A theorem is proposed to support the discussions. It is elaborated that establishing interconnections between the subsystems will help prevent undetectable attacks, and thus improving the systems’ safety.

Improving the safety of distributed cyber-physical systems against false data injection attack by establishing interconnections

Ground fault detection in inverter-based microgrid systems is challenging, particularly in a real-time setting, as the fault current deviates slightly from the nominal value. This difficulty is reinforced when natural disturbances exhibit similar output patterns as a faulty setting does. The conventional solution of installing more relays to obtain additional measurements is costly and also increases the complexity of the system. In this paper, we propose diagnosis schemes based on optimization-based fault detection filters with the output current as the only measurement. Modeling the microgrid dynamics and the diagnosis filter, we formulate the filter design as a linear programming (LP) problem that accounts for decoupling a class of disturbances and ensuring fault sensitivity simultaneously. Next, we robustify the filter to disturbances that cannot be fully decoupled. To this end, we leverage tools from the existing literature and extend the optimization program to a quadratic programming (QP) problem in which the filter is trained for this class of disturbances. To ease the computational effort, we also provide an approximate but analytical solution to this QP. Additionally, we use classical statistical results to provide a thresholding mechanism that enjoys probabilistic false-alarm guarantees. Finally, we verify the effectiveness of the proposed methods through several numerical simulations.

Jingwei Dong

Papers

A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes

Improving the safety of distributed cyber-physical systems against false data injection attack by establishing interconnections

Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems